System and method for increasing recognition rates of in-vocabulary words by improving pronunciation modeling

ABSTRACT

Disclosed herein are systems, methods, and computer readable-media for generating a lexicon for use with speech recognition. The method includes receiving symbolic input as labeled speech data, overgenerating potential pronunciations based on the symbolic input, identifying best potential pronunciations in a speech recognition context, and storing the identified best potential pronunciations in a lexicon. Overgenerating potential pronunciations can include establishing a set of conversion rules for short sequences of letters, converting portions of the symbolic input into a number of possible lexical pronunciation variants based on the set of conversion rules, modeling the possible lexical pronunciation variants in one of a weighted network and a list of phoneme lists, and iteratively retraining the set of conversion rules based on improved pronunciations. Symbolic input can include multiple examples of a same spoken word. Speech data can be labeled explicitly or implicitly and can include words as text and recorded audio.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to speech processing and more specificallyto speech recognition.

2. Introduction

Telephone based customer service is a tremendous expense for businesses.Many recent advances have facilitated automation of numerous aspects ofcustomer service in this sector, saving millions of dollars. Earlyattempts at automation involved speech synthesis by a computing deviceand user input in the form of button presses. More modem telephonesystem automation uses speech recognition to allow callers to interactmore naturally. However, accurate and dependable speech recognitionrelies on a transcription lexicon capable of converting between wordsand phonemes. Lexicon accuracy is one factor influencing recognitionaccuracy. A lexicon can be deficient if it fails to contain words to berecognized, or so called Out Of Vocabulary (OOV) words. The lexicon canalso be deficient if it contains inaccurate transcriptions, or only hasa single transcription for a word when the word is pronounceable in morethan one way, as is the case with many proper names. In an automatedspeech recognition system or an interactive voice response system,inaccurate speech recognition is extremely detrimental. Although suchsystems can save significant amounts of money when compared to hiringpeople to answer phones, if callers are frustrated by poor speechrecognition, the cost savings can be outweighed by loss of goodwill.

Accordingly, what is needed in the art is an improved way to generatelexica which allow for more accurate speech recognition.

SUMMARY

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. Thefeatures and advantages of the invention may be realized and obtained bymeans of the instruments and combinations particularly pointed out inthe appended claims. These and other features of the present inventionwill become more fully apparent from the following description andappended claims, or may be learned by the practice of the invention asset forth herein.

Disclosed are systems, methods, and tangible computer readable-media forgenerating a lexicon for use with speech recognition. The methodincludes receiving symbolic input as labeled speech data, overgeneratingpotential pronunciations based on the symbolic input, identifying bestpotential pronunciations in a speech recognition context, and storingthe identified best potential pronunciations in a lexicon.Overgenerating potential pronunciations can include establishing a setof conversion rules for short sequences of letters, converting portionsof the symbolic input into a number of possible lexical pronunciationvariants based on the set of conversion rules, modeling the possiblelexical pronunciation variants in one of a weighted network and a listof phoneme lists, and iteratively retraining the set of conversion rulesbased on improved pronunciations. Symbolic input can include multipleexamples of a same spoken word. Speech data can be labeled explicitly orimplicitly and can include words as text and recorded audio. Identifyingbest potential pronunciations can be based on a threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the invention and are not thereforeto be considered to be limiting of its scope, the invention will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1 illustrates an example system embodiment;

FIG. 2 illustrates an example method embodiment; and

FIG. 3 illustrates a process flow from labeled speech data to a lexicon.

DETAILED DESCRIPTION

Various embodiments of the invention are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the invention.

With reference to FIG. 1, an exemplary system includes a general-purposecomputing device 100, including a processing unit (CPU) 120 and a systembus 110 that couples various system components including the systemmemory such as read only memory (ROM) 140 and random access memory (RAM)150 to the processing unit 120. Other system memory 130 may be availablefor use as well. It can be appreciated that the invention may operate ona computing device with more than one CPU 120 or on a group or clusterof computing devices networked together to provide greater processingcapability. A processing unit 120 can include a general purpose CPUcontrolled by software as well as a special-purpose processor. An IntelXeon LV L7345 processor is an example of a general purpose CPU which iscontrolled by software. Particular functionality may also be built intothe design of a separate computer chip. An STMicroelectronics STA013processor is an example of a special-purpose processor which decodes MP3audio files. Of course, a processing unit includes any general purposeCPU and a module configured to control the CPU as well as aspecial-purpose processor where software is effectively incorporatedinto the actual processor design. A processing unit may essentially be acompletely self-contained computing system, containing multiple cores orCPUs, a bus, memory controller, cache, etc. A multi-core processing unitmay be symmetric or asymmetric.

The system bus 110 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 140 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 100, such as during start-up. The computing device 100further includes storage devices such as a hard disk drive 160, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 160 is connected to the system bus 110 by a driveinterface. The drives and the associated computer readable media providenonvolatile storage of computer readable instructions, data structures,program modules and other data for the computing device 100. In oneaspect, a hardware module that performs a particular function includesthe software component stored in a tangible computer-readable medium inconnection with the necessary hardware components, such as the CPU, bus,display, and so forth, to carry out the function. The basic componentsare known to those of skill in the art and appropriate variations arecontemplated depending on the type of device, such as whether the deviceis a small, handheld computing device, a desktop computer, or a computerserver.

Although the exemplary environment described herein employs the harddisk, it should be appreciated by those skilled in the art that othertypes of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs), read only memory (ROM), a cable or wireless signal containing abit stream and the like, may also be used in the exemplary operatingenvironment.

To enable user interaction with the computing device 100, an inputdevice 190 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. The input maybe used by the presenter to indicate the beginning of a speech searchquery. The device output 170 can also be one or more of a number ofoutput mechanisms known to those of skill in the art. In some instances,multimodal systems enable a user to provide multiple types of input tocommunicate with the computing device 100. The communications interface180 generally governs and manages the user input and system output.There is no restriction on the invention operating on any particularhardware arrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

For clarity of explanation, the illustrative system embodiment ispresented as comprising individual functional blocks (includingfunctional blocks labeled as a “processor”). The functions these blocksrepresent may be provided through the use of either shared or dedicatedhardware, including, but not limited to, hardware capable of executingsoftware and hardware, such as a processor, that is purpose-built tooperate as an equivalent to software executing on a general purposeprocessor. For example the functions of one or more processors presentedin FIG. 1 may be provided by a single shared processor or multipleprocessors. (Use of the term “processor” should not be construed torefer exclusively to hardware capable of executing software.)Illustrative embodiments may comprise microprocessor and/or digitalsignal processor (DSP) hardware, read-only memory (ROM) for storingsoftware performing the operations discussed below, and random accessmemory (RAM) for storing results. Very large scale integration (VLSI)hardware embodiments, as well as custom VLSI circuitry in combinationwith a general purpose DSP circuit, may also be provided.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits.

Having disclosed the elements of a basic system, the disclosure turns tothe method embodiment. The method is discussed in terms of a systemconfigured to practice the method. FIG. 2 illustrates an example methodembodiment. The system receives parallel symbolic and audio data as(202). Input can include multiple examples of a same spoken word. Thisvariety of rate, voice quality, accent, gender, age, and other speechsample attributes of the same spoken word in the training data can leadto a more robust lexicon. The labeled speech data includes text as wellas recorded audio. With fixed text and audio, the problem is one offorced recognition where the only variable to be modified is lexicalcontents. The system performs this modification with the goal ofincreasing the recognition score. Furthermore the system can performthese modifications without supervision.

The system overgenerates potential pronunciations based on the symbolicinput (204), meaning that the system generates far more pronunciationsthan what is needed. The system can use knowledge of how text is likelyto be spoken to limit the number of recognition candidates. The systemneeds to control overgeneration of potential pronunciations or elsespurious transcriptions, combinatorial problems, or other issues canarise. The system can convert text to phonemes using a method based onL&K[1]. One way to overgenerate potential pronunciations is to establisha set of conversion rules for short sequences of letters (204 a),convert portions of the symbolic input into a number of possible lexicalpronunciation variants based on the set of conversion rules (204 b), andmodel the possible lexical pronunciation variants as either a weightednetwork or a list of phoneme lists (204 c). The system can optionallyiteratively retrain the set of conversion rules based on improvedpronunciations. One conversion rule for short sequences of letters isconverting LM to /l/ /m/ (as in the name ‘Kilmartin’) and converting LMto /m/ (as in the word ‘palm’). The system can assign appropriatecontexts and/or weights to rules as an indication of how likely they areto be appropriate. Based on these rules the system converts the lettersof a word and into a number of possible lexical pronunciations. Undercertain circumstances modeling the lexical entries as weighted networksmakes more sense than modeling them as a long list of phoneme lists. Thesystem can then test these variants for suitability and retain the best.The system can then use information about the rules used to generate themost successful variants to modify weights associated with rules, usingan iterative approach, to further refine the rules used forvariant/candidate generation. In this manner, the system assigns agreater weight to more successful rules and a lesser weight to lesssuccessful rules. If a rule's weight is extremely low, the system candrop the rule altogether.

If a user or administrator notices that the system is creatingsub-optimal pronunciations, this sub-group of steps provides one way forthe administrator to fix the problem at the source. Conversion rules canbe retrained as needed. The system can substitute different sets ofconversion rules for specific applications. In other words, the same setof symbolic input can generate a very different lexicon in the end ifprocessed by a set of differently tuned rules. The system canparticipate in a more general iterative approach which retrains theacoustic models based on improved pronunciations over and over toachieve some level of convergence.

The system then identifies best potential pronunciations in a speechrecognition context (206). A dynamic or static threshold can helpidentify best potential pronunciations.

The system stores the identified best potential pronunciations in alexicon (208). The best potential pronunciations in the lexicon canenable higher speech recognition accuracy.

FIG. 3 illustrates a process flow from labeled speech data to a lexicon.Labeled speech data 302 is passed to a server 304. The server appliesconversion rules 306 to the speech data. If the conversion rules are notproducing acceptable conversion output, improved pronunciations 308 cantrain and retrain the conversion rules 306 iteratively. The conversionrules output a set of potential pronunciations 3 10. An identifier 312selects the best potential pronunciations and inserts them into alexicon 314 for use with speech recognition. This approach yields lexicathat are better suited to speech recognition and can increaserecognition accuracy. This approach can improve the automated speechrecognition general lexicon of names, help generate lexica for“Spanglish” or other pidgin languages.

Embodiments within the scope of the present invention may also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer, including the functional design ofany special purpose processor as discussed above. By way of example, andnot limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tocarry or store desired program code means in the form ofcomputer-executable instructions, data structures, or processor chipdesign. When information is transferred or provided over a network oranother communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, data structures, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of theinvention may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the invention.For example, the principles herein may be applied to any speechrecognition application that requires a lexicon. Those skilled in theart will readily recognize various modifications and changes that may bemade to the present invention without following the example embodimentsand applications illustrated and described herein, and without departingfrom the true spirit and scope of the present invention.

1. A method of generating a lexicon for use with speech recognition, themethod comprising: receiving symbolic input as labeled speech data;overgenerating potential pronunciations based on the symbolic input;identifying best potential pronunciations in a speech recognitioncontext; and storing the identified best potential pronunciations in alexicon.
 2. The method of claim 1, wherein symbolic input includesmultiple examples of a same spoken word.
 3. The method of claim 1,wherein speech data is labeled explicitly.
 4. The method of claim 1,wherein speech data is labeled implicitly.
 5. The method of claim 1,wherein labeled speech data includes words as text and recorded audio.6. The method of claim 1, wherein overgenerating potentialpronunciations comprises: establishing a set of conversion rules forshort sequences of letters; converting portions of the symbolic inputinto a number of possible lexical pronunciation variants based on theset of conversion rules; and modeling the possible lexical pronunciationvariants in one of a weighted network and a list of phoneme lists. 7.The method of claim 6, the method further comprising iterativelyretraining the set of conversion rules based on improved pronunciations.8. The method of claim 1, wherein identifying best potentialpronunciations is based on a threshold.
 9. A system for generating alexicon for use with speech recognition, the system comprising: a moduleconfigured to receive symbolic input as labeled speech data; a moduleconfigured to overgenerate potential pronunciations based on thesymbolic input; a module configured to identify best potentialpronunciations in a speech recognition context; and a module configuredto store the identified best potential pronunciations in a lexicon. 10.The system of claim 1, wherein symbolic input includes multiple examplesof a same spoken word.
 11. The system of claim 1, wherein speech data islabeled explicitly.
 12. The system of claim 1, wherein speech data islabeled implicitly.
 13. The system of claim 1, wherein labeled speechdata includes words as text and recorded audio.
 14. The system of claim1, wherein the module configured to overgenerate potentialpronunciations comprises: a module configured to establish a set ofconversion rules for short sequences of letters; a module configured toconvert portions of the symbolic input into a number of possible lexicalpronunciation variants based on the set of conversion rules; and amodule configured to model the possible lexical pronunciation variantsin one of a weighted network and a list of phoneme lists.
 15. The systemof claim 6, the system further comprising a module configured toiteratively retrain the set of conversion rules based on improvedpronunciations.
 16. A tangible computer-readable medium storing acomputer program having instructions for generating a lexicon for usewith speech recognition, the instructions comprising: receiving symbolicinput as labeled speech data; overgenerating potential pronunciationsbased on the symbolic input; identifying best potential pronunciationsin a speech recognition context; and storing the identified bestpotential pronunciations in a lexicon.
 17. The computer-readable mediumof claim 1, wherein symbolic input includes multiple examples of a samespoken word.
 18. The computer-readable medium of claim 1, wherein speechdata is labeled explicitly.
 19. The computer-readable medium of claim 1,wherein speech data is labeled implicitly.
 20. The computer-readablemedium of claim 1, wherein overgenerating potential pronunciationscomprises: establishing a set of conversion rules for short sequences ofletters; converting portions of the symbolic input into a number ofpossible lexical pronunciation variants based on the set of conversionrules; and modeling the possible lexical pronunciation variants in oneof a weighted network and a list of phoneme lists.